MSE Seminar Series
Friday, September 17, 2021 @12:20pm
*Remote course - Zoom link will be provided
Department of Materials Science & Engineering,
Identification of new battery chemistries guided by data science and multi-metric performance objectives
I will discuss our efforts to combine diverse data sets and materials property calculations with physics and intuition to identify new battery chemistries that satisfy a spectrum of desirable performance metrics. Traditional approaches to battery chemistry development involve the identification of one battery component and optimization of one or two of the desired properties of that component. This approach has been a barrier to the development of new batteries, which are systems of multiple interacting materials that require the simultaneous satisfaction of perhaps a dozen performance metrics and interfacial compatibility conditions. I will discuss our efforts to develop holistic screening techniques to identify promising solid electrolytes and cathodes that satisfy the full spectrum of desired properties, discovered through data science screening approaches, physics informed machine learning, and density functional theory simulations. Specifically, we have identified several new low cost sulfur based electrolytes that are superior to known sulfur electrolytes and have potential to be scaled up in manufacturing.,  We also identify several cathodes that are more compatible with solid electrolytes than currently studied cathodes. 
 A. D. Sendek, Q. Yang, E. D. Cubuk, K.-A. N. Duerloo, Y. Cui, and E. J. Reed, “Holistic computational structure screening of more than 12 000 candidates for solid lithium-ion conductor materials,” Energy Environ. Sci., vol. 10, no. 1, pp. 306–320, 2017, doi: 10.1039/C6EE02697D.
 A. D. Sendek et al., “Combining Superionic Conduction and Favorable Decomposition Products in the Crystalline Lithium–Boron–Sulfur System: A New Mechanism for Stabilizing Solid Li-Ion Electrolytes,” ACS Applied Materials & Interfaces, vol. 12, no. 34, pp. 37957–37966, 2020. B. Ransom, et al, “Two low expansion Li-ion cathode materials with promising multi-metric performance”, MRS Impact, in press.
Evan Reed is a faculty member in Materials Science and Engineering at Stanford University. He received a B.S. in applied physics from Caltech (1998) and PhD. in physics from MIT (2003). In 2004, he was an E. O. Lawrence Fellow and staff scientist at Lawrence Livermore National Laboratory before moving to Stanford in 2010. Evan Reed’s recent work focuses on atomic scale theory and modeling of 2D and other electronic materials, statistical learning for chemical and energy storage applications, structural phase changes, and high pressure shock wave compression. His group has pioneered the application of data science and machine learning approaches to materials selection problems within these application domains